Case Study: GoodShip achieves sub-second logistics analytics and secure AI agent workloads with MotherDuck

Goodship launches AI Transportation Analyst in under a week with MotherDuck

GoodShip, a platform for freight orchestration and procurement, faced significant challenges with its analytics backend as its customer base grew. Their Postgres database could not handle complex queries, leading to frequent timeouts and an inability to return results for large historical datasets, which degraded the user experience. They evaluated several alternatives before choosing MotherDuck for its serverless infrastructure and compatible SQL dialect.

MotherDuck provided a solution using its remote MCP server and hypertenancy architecture, which guaranteed strict data isolation with dedicated compute nodes and service accounts per customer. This enabled GoodShip to build its AI analytics agent, Laney, in under a week. The results were sub-second query performance, a massive improvement from frequent timeouts, and a serverless billing model that matched their bursty usage pattern. Customers responded rapturously to the new, instant analytics capabilities.

Goodship